Many methods of automatic face recognition are known in the art. In such methods, a computer extracts and processes facial features from a captured image to identify the person or people in the image, typically by comparison to a facial database.
A necessary precursor to face recognition in many application environments is face detection: processing a captured image to determine whether there are any faces in the image and, if so, determining the location and extent of each face. Face detection is also useful in other applications, such as content-based image retrieval, video coding, video conferencing, crowd surveillance, and intelligent human-computer interfaces. The human face is a dynamic object, however, and has a high degree of variability in its appearance, which makes face detection a difficult problem in computer vision.
A variety of approaches to the problem of face detection are known in the art and are surveyed in the above-mentioned provisional patent application. Representative algorithms are described in the following publications, which are incorporated herein by reference:    1) M.-H. Yang, et al., “Detecting Faces in Images: A Survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence 24(1), pages 34-58 (2002).    2) Paul Viola and Michael J Jones, “Robust Real-Time Face Detection,” International Journal of Computer Vision 57, pages 137-154 (2004).    3) Oliver Jesorsky, et al., “Robust Face Detection Using the Hausdorff Distance,” Third International Conference on Audio- and Video-based Biometric Person Authentication, pages 90-95 (Springer, Lecture Notes in Computer Science, LNCS-2091, Halmstad, Sweden, 6-8 Jun. 2001).    4) H. Rowley, et al., “Neural Network-Based Face Detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence 20(1), pages 23-38 (1998).    5) Zhang, C., and Zhang, Z., “A survey of recent advances in face detection,” Technical Report, Microsoft Research (2010).